84th AMS Annual Meeting

Monday, 12 January 2004: 5:00 PM
Comparing rawinsonde and model soundings to improve aircraft icing forecasts (Formerly Paper number 3.2)
Room 619/620
Cory A. Wolff, NCAR, Boulder, CO
Poster PDF (460.2 kB)
Information from two of the numerical weather prediction models run by the National Center for Environmental Prediction (NCEP) are used as inputs for newly-developed icing diagnosis and prognosis methods. The Current and Forecast Icing Potential (CIP and FIP, respectively) algorithms cover the CONUS and use Rapid Update Cycle (RUC) model output. The Current and Forecast Icing Potential Alaska (CIP- and FIP-AK) products use Alaska Eta model output. Since the accuracy of these products is dependent on the temperature and relative humidity forecasts, it is important to understand their accuracies. One way to do this is to compare model soundings with those measured from National Weather Service rawinsondes. These observations supply data throughout the depth of the atmosphere and are obtained in locations across the model domain. Differences arising from altitude or geography can be identified.

For both models, the nearest grid point to each rawinsonde site was determined and a vertical profile of temperature and relative humidity was extracted for valid times of 0000 and 1200 UTC. The differences between observations and model outputs were analyzed using several methods to assess the accuracy of the model soundings; biases and spreads were emphasized. For example, the data sets were broken into 10C bins to examine the model accuracy in various temperature ranges. There is a high interest in model performance in the 0 to 20C range, since this is where the vast majority of inflight icing conditions reside.

Biases and spreads in the temperature forecasts were less than those for the relative humidity data. In general, both the RUC and Alaska Eta models were warmer and drier than the observations, but to varying degrees depending on the temperature range and location. Geographic and temperature range biases were identified and will be discussed. In Alaska, two sounding sites were also found to have significant differences in representing cloudy conditions from other sites nearby. Some example cases from both datasets will be presented to better illustrate the differences.

Supplementary URL: